Min Xu1, Deng-Feng Zhang2, Rongcan Luo1, Yong Wu1, Hejiang Zhou3, Li-Li Kong4, Rui Bi3, Yong-Gang Yao5. 1. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China. 2. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China. Electronic address: zhangdengfeng@mail.kiz.ac.cn. 3. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China. 4. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China; Institute of Health Science, Anhui University, Hefei, Anhui, China. 5. Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China. Electronic address: yaoyg@mail.kiz.ac.cn.
Abstract
INTRODUCTION: Profiling the spatial-temporal expression pattern and characterizing the regulatory networks of brain tissues are vital for understanding the pathophysiology of Alzheimer's disease (AD). METHODS: We performed a systematic integrated analysis of expression profiles of AD-affected brain tissues (684 AD and 562 controls). A network-based convergent functional genomic approach was used to prioritize possible regulator genes during AD development, followed by functional characterization. RESULTS: We generated a complete list of differentially expressed genes and hub genes of the transcriptomic network in AD brain and constructed a Web server (www.alzdata.org) for public access. Seventeen hub genes active at the early stages, especially YAP1, were recognized as upstream regulators of the AD network. Cellular assays proved that early alteration of YAP1 could promote AD by influencing the whole transcriptional network. DISCUSSION: Early expression disturbance of hub genes is an important feature of AD development, and interfering with this process may reverse the disease progression.
INTRODUCTION: Profiling the spatial-temporal expression pattern and characterizing the regulatory networks of brain tissues are vital for understanding the pathophysiology of Alzheimer's disease (AD). METHODS: We performed a systematic integrated analysis of expression profiles of AD-affected brain tissues (684 AD and 562 controls). A network-based convergent functional genomic approach was used to prioritize possible regulator genes during AD development, followed by functional characterization. RESULTS: We generated a complete list of differentially expressed genes and hub genes of the transcriptomic network in AD brain and constructed a Web server (www.alzdata.org) for public access. Seventeen hub genes active at the early stages, especially YAP1, were recognized as upstream regulators of the AD network. Cellular assays proved that early alteration of YAP1 could promote AD by influencing the whole transcriptional network. DISCUSSION: Early expression disturbance of hub genes is an important feature of AD development, and interfering with this process may reverse the disease progression.
Authors: Jiansong Fang; Andrew A Pieper; Ruth Nussinov; Garam Lee; Lynn Bekris; James B Leverenz; Jeffrey Cummings; Feixiong Cheng Journal: Med Res Rev Date: 2020-07-13 Impact factor: 12.944